{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cf9a11e5",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3410558f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最近 2 年上涨月数/总月数：8/25 | 概率：32.00%\n",
      "最近 3 年上涨月数/总月数：15/37 | 概率：40.54%\n",
      "最近 5 年上涨月数/总月数：25/61 | 概率：40.98%\n",
      "最近 8 年上涨月数/总月数：42/97 | 概率：43.30%\n",
      "最近 10 年上涨月数/总月数：50/121 | 概率：41.32%\n",
      "最近 13 年上涨月数/总月数：72/157 | 概率：45.86%\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import akshare as ak\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "import numpy as np\n",
    "\n",
    "# ========== 参数设置 ==========\n",
    "symbol = \"同花顺\"\n",
    "year_spans = [2, 3, 5, 8, 10, 13]\n",
    "\n",
    "# ========== 获取数据 ==========\n",
    "today = datetime.datetime.today()\n",
    "full_start_date = (today - pd.DateOffset(years=max(year_spans))).strftime('%Y%m%d')\n",
    "end_date = today.strftime('%Y%m%d')\n",
    "\n",
    "\n",
    "df = ak.stock_zh_a_daily(symbol=\"sz300033\", start_date=full_start_date, end_date=end_date, adjust=\"qfq\")\n",
    "df['date'] = pd.to_datetime(df['date'])  # 用英文字段\n",
    "df = df[['date', 'open', 'close']]  # 保留必要字段\n",
    "df = df.sort_values(\"date\")\n",
    "\n",
    "# ========== 初始化图表数据容器 ==========\n",
    "bar_data = {}  # key: N_YEARS, value: 月份 -> 上涨次数\n",
    "\n",
    "# ========== 各周期上涨统计 ==========\n",
    "for N_YEARS in year_spans:\n",
    "    start_cut = today - pd.DateOffset(years=N_YEARS)\n",
    "    df_cut = df[df[\"date\"] >= start_cut].copy()\n",
    "\n",
    "    # 每月开盘与收盘\n",
    "    monthly_df = df_cut.groupby(df_cut[\"date\"].dt.to_period(\"M\")).agg({\n",
    "        \"open\": \"first\",\n",
    "        \"close\": \"last\"\n",
    "    }).reset_index()\n",
    "\n",
    "    monthly_df[\"month\"] = monthly_df[\"date\"].dt.month\n",
    "    monthly_df[\"up\"] = monthly_df[\"close\"] > monthly_df[\"open\"]\n",
    "\n",
    "    up_counts = monthly_df.groupby(\"month\")[\"up\"].sum()\n",
    "    total_counts = monthly_df.groupby(\"month\")[\"up\"].count()\n",
    "\n",
    "    total_up_count = up_counts.sum()\n",
    "    total_months = len(monthly_df)\n",
    "    overall_up_probability = total_up_count / total_months if total_months else 0\n",
    "\n",
    "    print(f\"最近 {N_YEARS} 年上涨月数/总月数：{total_up_count}/{total_months} | 概率：{overall_up_probability:.2%}\")\n",
    "\n",
    "    # 保存当前周期上涨次数\n",
    "    bar_data[N_YEARS] = up_counts\n",
    "\n",
    "# ========== 合并图表：分组柱状图 ==========\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "bar_width = 0.12\n",
    "months = np.arange(1, 13)\n",
    "offsets = np.linspace(-bar_width * len(year_spans) / 2, bar_width * len(year_spans) / 2, len(year_spans))\n",
    "\n",
    "for i, N_YEARS in enumerate(year_spans):\n",
    "    counts = bar_data.get(N_YEARS, pd.Series(0, index=range(1, 13)))\n",
    "    counts = counts.reindex(range(1, 13), fill_value=0)\n",
    "    plt.bar(months + offsets[i], counts.values, width=bar_width, label=f\"{N_YEARS}年\")\n",
    "\n",
    "plt.xticks(months, [f\"{m}月\" for m in months], rotation=-90)\n",
    "plt.xlabel(\"月份\")\n",
    "plt.ylabel(\"上涨次数\")\n",
    "plt.title(f\"{symbol} 不同周期每月上涨次数对比\")\n",
    "plt.legend(title=\"周期\")\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.6)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
